Smart factory platform for processing data obtained in continuous process

ABSTRACT

Disclosed is a smart factory platform for processing data obtained in a continuous process including a first process and a second process following the first process. The smart factory platform includes a distributed parallel processing system including at least one processing unit that generates mapping data by mapping a process identification (ID) to collection data collected from the continuous process and sorts the mapping data to generate sorting data, the process ID defining a process where the collection data occurs and the sorting data being generated for association processing between pieces of collection data collected from different processes; and a big data analysis system storing the sorting data with respect to the process ID.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the Korean Patent ApplicationsNo. 10-2016-0112865 filed on Sep. 1, 2017, No. 10-2016-0113151 filed onSep. 2, 2017, No. 10-2016-0113506 filed on Sep. 2, 2017, and No.10-2016-0113516 filed on Sep. 2, 2017, which are hereby incorporated byreference as if fully set forth herein.

BACKGROUND Field of the Invention

The present disclosure relates to factory data processing, and moreparticularly, to processing of data obtained in a continuous process.

Discussion of the Related Art

A plurality of processes for producing a finished product by using rawmaterials are continuously performed, and output products of therespective processes are combined with one another and are provided tosubsequent processes, or a state of an output product of a specificprocess is changed and the state-changed product is provided to asubsequent process. In this manner, a production method where theprocesses are associated with one another is referred to as a continuousprocess production method. Examples of representative industries usingthe continuous process production method include steel industry, energyindustry, paper industry, oil refining industry, etc.

In industries using the continuous process production method, unlikeindustries using a single process production method, since raw materialsor intermediate goods move at a high speed, a data obtainment period isshort, and the amount of data is large. Also, since a product isproduced in a factory environment including much noise, dust, water,and/or the like, a measurement error occurs frequently, and depending ona working method, intermediate goods are combined with one another or aposition of a material moves.

Therefore, industries using the continuous process production methodneed a system that processes much data in real time and processes piecesof data, generated in respective processes, through association betweenthe pieces of data.

However, a conventional factory data processing system (for example, asteel data processing system) disclosed in Korean Patent Publication No.10-2015-0033847 (title of invention: digital factory production capacitymanagement system based on real-time factory situation, published onApr. 2, 2015) processes and analyzes data generated in a single process,and for this reason, cannot process much data generated in a continuousprocess in real time and cannot also analyze a correlation betweenpieces of data generated in respective processes.

SUMMARY

Accordingly, the present disclosure is directed to provide a smartfactory platform that substantially obviates one or more problems due tolimitations and disadvantages of the related art.

An aspect of the present disclosure is directed to provide a smartfactory platform for processing data obtained in a continuous process.

Another aspect of the present disclosure is directed to provide a methodof controlling a load of a processing unit for processing data obtainedin a continuous process.

Another aspect of the present disclosure is directed to provide a methodof storing data obtained in a continuous process, based on a distributedfile system.

Another aspect of the present disclosure is directed to provide a methodof classifying data obtained in a continuous process into load data andno-load data to perform processing on the data.

Another aspect of the present disclosure is directed to provide a methodof dividing and storing data obtained in a continuous process by apredetermined data number unit.

Another aspect of the present disclosure is directed to provide a methodof performing in parallel an operation of processing data, obtained in acontinuous process, into a file.

Another aspect of the present disclosure is directed to provide a methodof receiving and storing data obtained in a continuous process, based onthe normal operation or not of a queue server storing the obtained data.

Additional advantages and features of the disclosure will be set forthin part in the description which follows and in part will becomeapparent to those having ordinary skill in the art upon examination ofthe following or may be learned from practice of the disclosure. Theobjectives and other advantages of the disclosure may be realized andattained by the structure particularly pointed out in the writtendescription and claims hereof as well as the appended drawings.

To achieve these and other advantages and in accordance with the purposeof the disclosure, as embodied and broadly described herein, there isprovided a smart factory platform for processing data obtained in acontinuous process including a first process and a second processfollowing the first process, the smart factory platform including: adistributed parallel processing system including at least one processingunit that generates mapping data by mapping a process identification(ID) to collection data collected from the continuous process and sortsthe mapping data to generate sorting data, the process ID defining aprocess where the collection data occurs and the sorting data beinggenerated for association processing between pieces of collection datacollected from different processes; and a big data analysis systemstoring the sorting data with respect to the process ID.

It is to be understood that both the foregoing general description andthe following detailed description of the present disclosure areexemplary and explanatory and are intended to provide furtherexplanation of the disclosure as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosure and are incorporated in and constitute apart of this application, illustrate embodiments of the disclosure andtogether with the description serve to explain the principle of thedisclosure. In the drawings:

FIG. 1 is a diagram illustrating a smart factory architecture accordingto an embodiment of the present disclosure;

FIG. 2 is a block diagram illustrating a configuration of a middlewaresystem according to an embodiment of the present disclosure;

FIG. 3 is a diagram illustrating a configuration of a middleware systemincluding a plurality of processing units and a plurality of queuestorages;

FIG. 4 is a diagram illustrating in detail a configuration of adistributed parallel processing system according to an embodiment of thepresent disclosure;

FIG. 5 is a diagram illustrating a distributed parallel processingsystem including a plurality of processing units and a plurality ofmemories;

FIG. 6 is a conceptual diagram exemplarily illustrating a distributedparallel processing method applied to a data mapping and sorting job;

FIG. 7 is a diagram illustrating a configuration of a triplicatedmemory;

FIG. 8 is a diagram illustrating in detail a configuration of a big dataanalysis system according to an embodiment of the present disclosure;

FIG. 9 is a diagram illustrating in detail a configuration of a big dataanalysis system according to another embodiment of the presentdisclosure; and

FIG. 10 is a diagram illustrating an example of load data and no-loaddata.

DETAILED DESCRIPTION OF THE DISCLOSURE

Reference will now be made in detail to the exemplary embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

Hereinafter, embodiments of the present disclosure will be described indetail with reference to the accompanying drawings.

The terms described in the specification should be understood asfollows. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. The terms “first” and “second” are fordifferentiating one element from the other element, and these elementsshould not be limited by these terms.

It will be further understood that the terms “comprises”, “comprising,”,“has”, “having”, “includes” and/or “including”, when used herein,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The term “at least one” should be understood as including any and allcombinations of one or more of the associated listed items. For example,the meaning of “at least one of a first item, a second item, and a thirditem” denotes the combination of all items proposed from two or more ofthe first item, the second item, and the third item as well as the firstitem, the second item, or the third item.

FIG. 1 is a diagram illustrating a smart factory architecture accordingto an embodiment of the present disclosure.

As illustrated in FIG. 1, the smart factory architecture according to anembodiment of the present disclosure may include a data collectionsystem 1, a network system 2, and a smart factory platform 1000.

The data collection system 1 collects data generated in a continuousprocess. The continuous process may denote a process where a pluralityof processes for producing a finished product by using raw materials arecontinuously performed, and output products of the respective processesare combined with one another and are provided to subsequent processes,or a state of an output product of a specific process is changed and thestate-changed product is provided to a subsequent process. Arepresentative example of the continuous process may include a steelprocess. Hereinafter, for convenience of description, the continuousprocess is assumed as the steel process and will be described.

The steel process may include various processes such as an iron makingprocess, a steel making process, a continuous casting process, and arolling process. The data collection system 1 may collect microdatagenerated in an operation of performing the various processes such asthe iron making process, the steel making process, the continuouscasting process, and the rolling process. Here, the microdata may bedata itself obtained from various sensors and may denote raw data.Hereinafter, for convenience of description, microdata obtained in acontinuous process may be referred to as collection data.

The data collection system 1 may include various measuring instruments,sensors, and actuators, for collecting data generated in the continuousprocess. The data collection system 1 may further include a programmablecontroller (P/C), a programmable logic controller (PLC), and adistributed control system (DCS), which integrate or control dataobtained from the measuring instrument, the sensors, and the actuator.

The network system 2 transfers the collection data to the smart factoryplatform 1000. The network system 2 may include a network cable, agateway, a router, an access point (AP), and/or the like.

The smart factory platform 1000 receives the collection data through thenetwork system 2. The smart factory platform 1000 may process thecollection data, determine whether equipment, materials, and/or the likeare normal or not, based on the processed collection data, and provide asearch and analysis service for stored data.

In an embodiment, as illustrated in FIG. 1, the smart factory platform1000 according to the present disclosure may include a middleware system100, a distributed parallel processing system 200, and a big dataanalysis system 300.

The middleware system 100 may preprocess the collection data. Themiddleware system 100 may be connected to level 0 to level 2 devices.

To provide a more detailed description with reference to FIG. 2, themiddleware system 100 may include an interface unit 110 and a queueserver 120. Also, the middleware system 100 may further include amiddleware manager 130 and a queue manager 140.

The interface unit 110 may preprocess pieces of collection data forperforming association processing of the pieces of collection data. Theinterface unit 110 may standardize the collection data to preprocess thecollection data. To this end, the interface unit 110 may include atleast one of a parsing unit 112, a standardization unit 113, a filteringunit 114, and a transmission unit 115.

The parsing unit 112 may parse the collection data to generate parsingdata. The collection data may have a structure where a groupidentification (ID) including a plurality of item IDs, a collectiontime, and a plurality of measurement values are repeated. In this case,each of the item IDs is for identifying a measured attribute and may bea value representing which attribute of attributes of equipment,materials, and products has been measured, and for example, may be atemperature or humidity. The group ID may be a representative valuewhere some items are grouped by positions or by processes in a specificprocess. The group ID may include the collection time.

When the collection data is received in a structure where the group ID,the collection time, and the plurality of measurement values arerepeated without separate classification, the parsing unit 112 may parsethe collection data based on a predetermined layout, for associationprocessing of the collection data.

The parsing unit 112 may parse the collection data by group IDs and maymatch the plurality of measurement values with the plurality of item IDsincluded in the group ID to generate the parsing data having a structurewhich includes a single item ID, a collection time, and a singlemeasurement value.

The parsing unit 112 may parse the collection data, based on a messagelayout of a collection data full text.

The standardization unit 113 may standardize the parsing data togenerate standardization data. The standardization unit 113 may convertan item ID included in each of pieces of parsing data into a standarditem ID according to a predetermined standard conversion criterion foreach parsing data and may integrate a unit and a digit number of ameasurement value included in each parsing data, thereby standardizingthe parsing data. In this case, the predetermined standard conversioncriterion may include standard item IDs, set by item IDs of varioussensors, and a reference unit and a digit number of each of the standarditem IDs.

The standardization unit 113 may convert the item ID included in eachparsing data into the standardization item ID in order for pieces ofdata having the same measured attribute to have the same item ID.

The standardization unit 113 may preprocess the parsing data in orderfor some pieces of data, having the same measured attribute among piecesof parsing data, to have the same standard item ID, thereby enablingassociation processing to be performed on pieces of data obtained in acontinuous process, based on the standard item ID.

The filtering unit 114 may select standardization data, which is to bestored in the queue server 120, from among pieces of standardizationdata according to a predetermined filtering criterion. For example, agrade may be previously set based on the kind of the standardizationdata, and the filtering unit 114 may select the standardization datawhich is to be stored in the queue server 120, based on the grade. In anembodiment, the grade may be determined based on significance withrespect to the standard item ID of the standardization data. Thefiltering unit 114 may transmit the selected standardization data to thetransmission unit 115.

The transmission unit 115 may store the standardization data, providedfrom the filtering unit 114, in the queue server 120. The transmissionunit 115 may store, by group IDs or standard item IDs, thestandardization data in a queue storage 121 of the queue server 120.

The transmission unit 115 may store the standardization data in a queuestorage 121 which is small in load, based on loads of a plurality ofqueue storages 121. In another embodiment, if a queue server 120 inwhich the standardization data is to be stored by factories or processesis previously set among a plurality of queue servers 120, thetransmission unit 115 may store the standardization data in a queueserver 120 which is previously set for corresponding standardizationdata.

The transmission unit 115 may determine whether to store thestandardization data, based on an operation mode of the interface unit110. In detail, when the operation mode of the interface unit 110 is anormal mode, the transmission unit 115 may periodically store thestandardization data in the queue server 120, and when the operationmode of the interface unit 110 is a standby mode, the transmission unit115 may stop storing of the standardization data. In this case, theoperation mode of the interface unit 110 may be determined based on thenumber of queue servers, operating normally, of the plurality of queueservers 120.

The interface unit 110 may further include a data mergence unit 116. Thedata mergence unit 116 may merge the collection data to transfer themerged collection data to the parsing unit 112, so as to enhance dataprocessing performance. In an embodiment, the data mergence unit 116 maymerge the collection data which is received at a certain time interval(for example, 0.1 sec, 1 min, or the like).

In terms of a characteristic of the continuous process, the collectiondata may be transferred to the parsing unit 112 at a very short period(for example, 5 ms to 20 ms). Therefore, the data mergence unit 116 mayimmediately transfer collection data necessary for monitoring to theparsing unit 112 without being merged and may merge the other collectiondata at a certain time interval to transfer the merged collection datato the parsing unit 112.

In this case, whether the collection data is necessary for monitoring ornot may be set based on a significance of the collection data. Forexample, when an error occurs, collection data obtained from equipmentor materials requiring an immediate action may be set as collection datanecessary for monitoring.

In an embodiment, the middleware system 100 may further include themiddleware manager 130 and the queue manager 140, for managing theoperation mode of the interface unit 110.

In the middleware manager 130, an operation check unit 131 may determinewhether an operation of the queue server 120 is a normal operation ornot, and a mode management unit 132 may determine the operation mode ofthe interface unit 110.

Therefore, an availability of the interface unit 110 increases, and whenan error of the queue server 120 occurs, an active action may beperformed to prevent a secondary error of the interface unit 110.

The operation check unit 131 may determine whether the plurality ofqueue servers 120 operate normally or not. In an embodiment, theoperation check unit 131 may determine whether the plurality of queueservers 120 operate normally or not, based on a response to a testsignal. When the response to the test signal is not provided or apredefined response is not received from a corresponding queue server120, the operation check unit 131 may determine that the correspondingqueue server 120 does not normally operate.

The mode management unit 132 may determine the operation mode of theinterface unit 110, based on an operating state of each of the pluralityof queue servers 120. The mode management unit 132 may transfer anoperation mode of interface unit 110 to the interface unit 110.

In an embodiment, the mode management unit 132 may determine theoperation mode of the interface unit 110, based on the number ofnormally operating queue servers 120 among the plurality of queueservers 120. In detail, the mode management unit 132 may compare theamount of collection data received from the data collection system 1 andthe number of the normally operating queue servers 120, and when theamount of the received collection data does not exceed the number of thenormally operating queue servers 120, the mode management unit 132 maydetermine the operation mode of the interface unit 110 as a normal mode.In this case, the amount of the collection data may denote an average ofthe amount of collection data received in real time and the amount ofperiodically received collection data.

For example, the mode management unit 132 may determine the operationmode of the interface unit 110 as shown in the following Table 1.

TABLE 1 Number of Number of normally abnormally Number of operatingoperating queue storages queue storages queue storages Operation mode 33 0 Operation mode 3 2 1 Caution mode 3 1 2 Standby mode 3 0 3 Standbymode

When the operation mode of the interface unit 110 is determined as thenormal mode by the mode management unit 132, the interface unit 110 maystore the standardization data in a predetermined queue server 120 ofthe plurality of queue servers 120. When the operation mode of theinterface unit 110 is determined as the caution mode by the modemanagement unit 132, the interface unit 110 may store thestandardization data in queue servers 120 other than a queue server 120which abnormally operates. When the operation mode of the interface unit110 is determined as the standby mode by the mode management unit 132,the interface unit 110 may stop receiving of the collection data andstoring of the standardization data.

In this case, the caution mode may denote an operation mode where someof the plurality of queue servers 120 do not normally operate, but theinterface unit 110 is capable of storing the standardization data in theother queue servers 120 in real time. When two or more of the pluralityof queue servers 120 operate normally, the mode management unit 132 maydetermine the operation mode as the caution mode. The queue managementunit 140 may be provided to correspond to each of the plurality of queueservers 120.

The queue management unit 140 may manage metadata corresponding to eachof the plurality of queue servers 120 and may check whether theplurality of queue servers 120 operate normally or not. To this end, thequeue management unit 140 may include a metadata management unit 141.

The metadata management unit 141 may manage metadata corresponding tothe queue storage 121 of a corresponding queue server 120. For example,the metadata management unit 141 may manage metadata such asconfiguration information such as basic specification information,access information, topics, and partitions and may provide informationabout a topic and a partition, where data is to be stored, to theinterface unit 110 based on the metadata.

When some of the plurality of queue servers 120 do not normally operate,the interface unit 110 may quickly detect a location of a data-storablequeue server 120 by using metadata of the queue servers 120 to store theparsing data.

The queue server 120 may temporarily store the standardization databefore processing the standardization data in real time. To this end,the queue server 120 may include at least one queue storage 121.

The queue storage 121 may be a storage for storing the standardizationdata for a certain time and may store data based on a disk instead of amemory, for preventing a loss of the data. A space of the queue storage121 storing data may be divided by topics, and the queue storage 121 maydivide a partition in the same topic into a plurality of partitions,thereby allowing the data to be processed in parallel.

The queue server 120 may be provided in plurality, and the plurality ofqueue servers 120 may be clustered. In this case, if the transmissionunit 115 stores the standardization data in one of the plurality ofqueue servers 120, the same standardization data may be stored in theother queue servers 120.

In an embodiment, the standardization data stored in the queue server120 may be allocated a unique group ID for each data group which thedistributed parallel processing system 200 fetches from the queue server120. Accordingly, data fetch addresses may be managed by unique groupIDs, and thus, data may be stored and provided in a queue form ofsequentially reading and writing data.

In this case, a plurality of interface units 110 may be more implementedby adding the interface unit 110 depending on a scale of the datacollection system 1 and a physical position of a factory, and each ofthe interface units 110 may be implemented in a double structure forhigh availability (HA).

Moreover, when standardization of collection data is completed, theinterface unit 110 may select one queue server 120 from among theplurality of queue servers 120 and may store standardization data in theselected queue server 120. In this case, a criterion for selecting aqueue server 120 in which the standardization data is to be stored maybe selected from among various rules, and for example, a queue server120 which is lowest in load may be selected, queue servers 120 may besequentially selected, or a queue server 120 for storing thestandardization data may be previously selected and stored for eachsensor from which collection data is obtained.

Moreover, each of the interface units 110 may include the middlewaremanager 130. Each of a plurality of middleware managers 130 maydetermine whether the plurality of queue storages 120 operate normallyor not and may determine an operation mode of a corresponding interfaceunit 110.

The distributed parallel processing system 200 may processstandardization data transferred from the middleware system 100. In anembodiment, the distributed parallel processing system 200 may generatemapping data where the standardization data is mapped to a process ID,and may sort pieces of mapping data in each process, for associationprocessing of pieces of collection data collected in each process.

To provide a detailed description with reference to FIG. 4, thedistributed parallel processing system 200 may include a processing unit210 and a memory 220.

The processing unit 210 may map a process ID to standardization data togenerate mapping data and may sort the mapping data so as to enableassociation analysis to be performed on inter-area data such asmanufacturing-equipment-quality. Also, the processing unit 210 maypredict omission data which is omitted in the middle of a collectionperiod or at a point where data is not obtained because there is nosensor.

To this end, the processing unit 210 may include at least one of a fetchperforming unit 211, a process mapping performing unit 213, a datacorrection performing unit 215, and a data sorting performing unit 216.Also, the processing unit 210 may further include an equipment errorsensing performing unit 217 and a quality error sensing performing unit218.

In an embodiment, a plurality of performing units 211 to 218 illustratedin FIG. 4 may be respectively implemented with applications which aredistributed to the processing unit 210 and realize their functions. Theapplications may generate a working space in the processing unit 210 andmay generate a plurality of threads, thereby performing the respectivefunctions allocated for each of the performing units 211 to 218.

The fetch performing unit 211 may read standardization data from thequeue storage 121 of the middleware system 100 and may store thestandardization data in the memory 220. The fetch performing unit 211may memorize location information obtained for previously searching fordata of the plurality of queues storages 121, and thus, may read datanext to previously-read data.

In this case, when the interface unit 110 stores, by group IDs orstandard item IDs, standardization data in the queue storage 121 forassociation processing of pieces of collection data obtained in thecontinuous process, the fetch performing unit 211 may read, by group IDsor standard item IDs, the standardization data stored in the queuestorage 121.

The process mapping performing unit 213 may map the standardizationdata, read by the fetch performing unit 211, to a process ID foridentifying a process where the standardization data is obtained,thereby generating mapping data.

In an embodiment, the process mapping performing unit 213 may map anequipment ID of equipment performing each process to the standardizationdata to generate first mapping data, or may map a material ID of amaterial processed by the equipment to the standardization data or thefirst mapping data to generate second mapping data. To this end, themapping performing unit 213 may include an equipment mapping performingunit 213 a and a material mapping performing unit 213 b.

The equipment mapping performing unit 213 a may map an equipment ID ofequipment, from which the standardization data is obtained, to thestandardization data to generate first mapping data. The equipmentmapping performing unit 213 a may obtain the equipment ID which is to bemapped to the standardization data, based on a collection time when thestandardization data is collected or attribute information about asensor from which the standardization data is obtained. In anembodiment, the equipment ID may be an equipment number which isassigned for each of equipment.

The material mapping performing unit 213 b may map a material ID of amaterial, processed by equipment from which correspondingstandardization data is obtained, to the corresponding standardizationdata read from the memory 220 or the first mapping data generated by theequipment mapping performing unit 213 a to generate the second mappingdata. The material mapping performing unit 213 b may obtain a materialID of a material generated through equipment from which correspondingstandardization data is obtained, based on job instruction informationfor instructing a job performed in each process and may map the obtainedmaterial ID to the first mapping data.

In an embodiment, the material ID may be a material number which isassigned for each material.

The standardization data may include load data collected in a processedstate of a material and no-load data collected in a non-processed stateof the material. The material mapping performing unit 213 b mayimmediately store the load data, mapped to an equipment ID, in a sortingdata storage 222. In this case, the load data and the no-load data maybe divisionally stored in the sorting data storage 222.

The data correction performing unit 215 may add data omitted from amongpieces of mapping data to correct the mapping data. The data correctionperforming unit 215 may correct the omitted data by using mapping data,corresponding to a position closest to an area where the omitted datashould be collected, and mapping data corresponding to a collection timeclosest to a time when the omission occurs.

In an embodiment, the data correction performing unit 215 may match acollection time, included in mapping data, with a predeterminedcollection period so as to correct the mapping data. For example, whencontinuous process data is stored at a collection period of 20 ms, thedata correction performing unit 215 may correct a collection time ofmapping data, of which the collection time is 15:01:11:0005 ms, to15:01:11:000 ms and may correct a collection time of mapping data, ofwhich the collection time is 15:01:11:0050 ms, to 15:01:11:0040 ms.

The data sorting performing unit 216 may sort mapping data or correctedmapping data, for association processing of pieces of data of respectiveprocesses.

The data sorting performing unit 216 may sort, by a material unit,pieces of mapping data mapped to the same material ID in a time order togenerate first sorting data, for association processing of collectiondata obtained in a continuous process.

The data sorting performing unit 216 may sort pieces of first sortingdata with respect to a collection position, from which correspondingdata is collected, in a material corresponding to the same material IDto generate second sorting data.

In this case, the collection position may be determined based on atleast one of a length of the material, a moving speed of the material,and a collection period of collection data. For example, the datasorting performing unit 216 may determine a collection position, fromwhich the collection data is collected, in a material at every period,based on a value obtained by multiplying the collection period and themoving speed of the material and a total length of the material.Therefore, the data sorting performing unit 216 may sort the firstsorting data to data which is measured at a certain position in onedirection in the material.

The data sorting performing unit 216 may calculate a measurement valueat each of reference points, based on a distance between the referencepoints arranged at predetermined intervals in each of materials andcollection positions of pieces of second sorting data and may generatereference data at each reference point based on the calculatedmeasurement value, for association processing of pieces of collectiondata which are collected from a first process and a second process atdifferent periods.

The data sorting performing unit 216 may sequentially sort pieces ofreference data at the reference points and the second sorting data inone direction. In an embodiment, the one direction may be at least oneof a lengthwise direction of a material, a widthwise direction of thematerial, and a thickness direction of the material.

Hereinafter, an example where the data sorting performing unit 216 sortspieces of reference data in a lengthwise direction in a material will bedescribed in detail.

A plurality of first reference points may be arranged at certainintervals in a lengthwise direction of a first material which has beenprocessed in a first process, and a plurality of second reference pointsmay be arranged at certain intervals in a lengthwise direction of asecond material which has been processed in a second process. In thiscase, a first material ID corresponding to the first material may bemapped to pieces of first reference data at the first reference points,and a second material ID corresponding to the second material may bemapped to pieces of second reference data at the second referencepoints. Therefore, the pieces of first reference data and the pieces ofsecond reference data may be associated with one another in a materialfamily tree (not shown) which is mapped to a material ID for eachmaterial, based on the first material ID and the second material ID.

That is, a plurality of material IDs may be linked as a tree type in thematerial family tree, and thus, by referring to the material familytree, pieces of mapping data of respective processes may be associatedwith one another, based on a material ID allocated to a material whichis generated by sequentially undergoing the first process and the secondprocess.

The data sorting performing unit 216 may store the second sorting dataand the pieces of reference data, which are sorted in a lengthwisedirection of a material as described above, in the memory 220.

As described above, the processing unit 210 may map a process ID such asan equipment ID or a material ID to standardization data and may sortpieces of mapping data, thereby enabling association processing to beperformed on pieces of collection data obtained in the continuousprocess.

The equipment error sensing performing unit 217 may receive the firstmapping data from the equipment mapping performing unit 213 a and maydetermine whether equipment is normal or not, based on a predeterminedequipment error determination criterion. When it is determined as aresult of the determination that an error occurs in specific equipment,the equipment error sensing performing unit 217 may store thedetermination result in the memory 220.

The quality error sensing performing unit 218 may determine whetherquality is normal or not, based on a quality error determinationcriterion predetermined based on the second sorting data sorted by thedata sorting performing unit 216. When it is determined as a result ofthe determination that an error occurs in a quality of a specificmaterial, the quality error sensing performing unit 218 may store thedetermination result in the memory 220.

In an embodiment, the quality error sensing performing unit 218 maygenerate macrodata, which is to be used as a reference of a qualityerror determination equation, through an operation such as prediction ofan average and an error of the second sorting data and may substitutethe second sorting data into the quality error determination equation todetermine the occurrence or not of a quality error according to a resultof the substitution.

In the above-described embodiment, it has been described above that thedistributed parallel processing system 200 may map and sortstandardization data by using one processing unit 210 and one memory220. In a modification embodiment, however, the distributed parallelprocessing system 200 may map and sort standardization data by using aplurality of processing units 210 a to 210 c and a plurality of memories220 as illustrated in FIG. 5.

Hereinafter, a distributed parallel processing system according to amodification embodiment will be described with reference to FIGS. 4 and5.

FIG. 5 is a diagram illustrating a distributed parallel processingsystem 200 including a plurality of processing units and a plurality ofmemories. The distributed parallel processing system 200 may include aplurality of processing units 210 a to 210 c, a plurality of memories220 a to 220 c, and a performing unit manager 230.

One or more of a plurality of performing units 211 to 218 for mappingand sorting standardization data may be distributed to the plurality ofprocessing units 210 a to 210 c. The plurality of processing units 210 ato 210 c may distribute and parallel process at least one of a fetchperforming unit 211, an equipment mapping performing unit 213 a, amaterial mapping performing unit 213 b, a data correction performingunit 215, a data sorting performing unit 216, an equipment error sensingperforming unit 217, and a quality error sensing performing unit 218 toperform parallel processing and may store final result data in thememory 220, thereby processing standardization data transferred from themiddleware system 100 in real time.

In an embodiment, the plurality of processing units 210 a to 210 c maybe configured in a cluster structure. As described above, the pluralityof processing units 210 a to 210 c may have the cluster structure, andthus, when an error occurs in a specific processing unit, the performingunits 211 to 218 which are being executed in the specific processingunit having the error may move to another processing unit, therebysecuring availability.

The plurality of memories 220 may store data processed by the pluralityof processing units 210 a to 210 c. In an embodiment, in order toincrease processing performance and ensure availability when an erroroccurs, the plurality of memories 220 may have a cluster structure likethe above-described queue storages 120.

The plurality of memories 220 may be provided in a double structure forhigh availability (HA). That is, each of the plurality of memories 220may include a master instance M and a slave instance S. In this case, amaster instance M included in a first memory 220 a and a slave instanceS included in a second memory 220 b may operate in pairs, and a masterinstance M included in the second memory 220 b and a slave instance Sincluded in the first memory 220 a may operate in pairs.

Sorting data stored in the slave instance S may be backed up as ascripter-form file, for recovering the sorting data when an erroroccurs. In this case, the scripter-form file may denote a file where acommand associated with writing or reading of the data is stored alongwith the data.

The master instance M and the slave instance S of each memory 220 may beconfigured in a single thread form, and instances and ports may beseparated from each other for each writing and reading.

Hereinafter, a method of performing distributed parallel processing onan operation of mapping and sorting standardization data will bedescribed with reference to FIG. 6 for example.

As illustrated in FIG. 6, since the fetch performing unit 211 isdistributed to the first processing unit 210 a, by executing the fetchperforming unit 211, the first processing unit 210 a may allow the fetchperforming unit 211 to access the queue storage 121, fetchstandardization data, and store the fetched data in the master instanceM of the first memory 220 a. In this case, data may also be copied toand stored in the slave instance S of the second memory 220 b. Since theequipment mapping performing unit 213 a and the material mappingperforming unit 213 b are distributed to the second processing unit 210b, by executing the equipment mapping performing unit 213 a, the secondprocessing unit 210 b may map an equipment ID to standardization dataread from the slave instance S of the second memory 220 b or the slaveinstance S of the first memory 220 a, and by executing the materialmapping performing unit 213 b, the second processing unit 210 b may mapa material ID to the standardization data or the first mapping data towhich the equipment ID is mapped.

Since the data correction performing unit 215 and the data sortingperforming unit 216 are distributed to the third processing unit 210 c,the third processing unit 210 c may execute the data correctionperforming unit 215 to correct mapping data omitted from among pieces ofmapping data and may execute the data sorting performing unit 216 tosort the pieces of mapping data or the corrected mapping data by amaterial unit and store the sorted mapping data in the master instance Mof the second memory 220 b. In this case, data may also be stored in theslave instance S of the first memory 220 a.

In the above-described embodiment, it has been described that since theplurality of memories 220 are configured in a double structure, themaster instance M included in the first memory 220 a and the slaveinstance S included in the second memory 220 b operate in pairs, and themaster instance M included in the second memory 220 b and the slaveinstance S included in the first memory 220 a operate in pairs.

In such an embodiment, however, the master instance M and the slaveinstance S may each be implemented as a single thread, and thus, whenthe master instance M of the first memory 220 a is downed, the slaveinstance S of the second memory 220 b cannot service all of a writingoperation and a reading operation for a downtime taken until the masterinstance M of the first memory 220 is normalized.

Therefore, in a modification embodiment, as illustrated in FIG. 7, thememories 220 may be implemented in a triple structure. In detail, eachof the memories 220 according to a modification embodiment may include amaster instance M, a first slave instance S1, and a second slaveinstance S2.

The master instance M included in the first memory 220 a and the firstslave instance S1 of each of the second and third memories 220 b and 220c may operate in pairs. Therefore, when data is written in the masterinstance M included in the first memory 220 a, data may also be copiedto and stored in the first slave instance S1 of each of the second andthird memories 220 b and 220 c.

Moreover, the master instance M included in the second memory 220 b mayoperate in pairs along with the first slave instance S1 included in thefirst memory 220 a and the second slave instance S2 included in thethird memory 220 c. Therefore, when data is written in the masterinstance M included in the second memory 220 b, data may also be storedin the first slave instance S1 included in the first memory 220 a andthe second slave instance S2 included in the third memory 220 c.

Moreover, the master instance M included in the third memory 220 c andthe second slave instances S2 respectively included in the first andsecond memories 220 a and 220 b may operate in pairs. Therefore, whendata is written in the master instance M included in the third memory220 c, data may also be copied to and stored in the second slaveinstances S2 respectively included in the first and second memories 220a and 220 b.

The performing unit manager 230 may distribute the plurality ofperforming units 211 to 218 to the plurality of processing units 210 ato 210 c. Also, the performing unit manager 230 may redistribute theplurality of performing units 211 to 218 to the plurality of processingunits 210 a to 210 c, based on a load amount of each of the first tothird processing units 210 a to 210 c according to execution of theperforming units 211 to 218 distributed to the first to third processingunit 210 a to 210 c.

The performing unit manager 230 may include a performing unit storage232, a distribution order determiner 234, and a distributer 236.

The plurality of performing units 211 to 218 for performing an operationof mapping and sorting standardization data may be stored in theperforming unit storage 232.

The distribution order determiner 234 may determine resource useinformation about the processing units 210 a to 210 c after theplurality of performing units 211 to 218 are distributed to each of theprocessing units 210 a to 210 c by the distributer 236, and maydetermine a distribution order in which the plurality of performingunits 211 to 218 are redistributed, so as to enable the load amounts ofthe processing units 210 a to 210 c to be controlled. In an embodiment,the distribution order determiner 234 may determine the distributionorder in which the plurality of performing units 211 to 218 areredistributed, so as to enable the load amounts of the processing units210 a to 210 c to become equal. Here, the determination of thedistribution order may denote determining the processing units 210 a to210 c which each of the performing units 211 to 218 are to beredistributed.

In another embodiment, the distribution order determiner 234 maydetermine a distribution order in which the plurality of performingunits 211 to 218 are distributed, based on at least one of a use patternof system resources and an average value of the system resources basedon execution of an initially distributed performing unit. In such anembodiment, the system resources may include at least one of a CPU userate, a memory use amount, a network communication amount, and a diskinput/output throughput of each of the processing units 210 a to 210 c.

The distributer 236 may distribute the plurality of performing units 211to 218 to the plurality of processing units 210 a to 210 b, based on thedistribution order determined by the distribution order determiner 234.

In detail, the distributer 236 may arbitrarily distribute the pluralityof performing units 211 to 218, stored in the performing unit storage232, to the processing units 210 a to 210 c. Subsequently, when apredetermined idle period arrives, the distributer 236 may collect theplurality of performing units 211 to 218 distributed to each of theprocessing units 210 a to 210 c and may store the collected performingunits 211 to 218 in the performing unit storage 232, and when the idleperiod ends, the distributer 236 may distribute the plurality ofperforming units 211 to 218 to a corresponding processing unit of theprocessing units 210 a to 210 c, based on the distribution orderdetermined by the distribution order determiner 234.

The big data analysis system 300 may store sorting data, sorted by thedistributed parallel processing system 200, in a big data storage space.Also, the big data analysis system 300 may manage data not to be lostand may provide a search function for historical data. Hereinafter, thebig data analysis system 300 according to an embodiment of the presentdisclosure will be described in detail with reference to FIG. 8.

FIG. 8 is a diagram illustrating in detail a configuration of a big dataanalysis system 300 according to an embodiment of the presentdisclosure. The big data analysis system 300 may include a dataprocessing unit 310, a big data storage 320, and a query processing unit330.

The data processing unit 310 may perform distributed parallel processingon sorting data and an error sensing result and may include at least oneof a completion event reception unit 311, a sorting data fetch unit 312,a memory queue 313, a file creation unit 314, and an error sensing datareception unit 315.

The completion event reception unit 311 may monitor the memory 220 ofthe distributed parallel processing system 200, and when a completionevent is newly stored, the completion event reception unit 311 maytransfer the completion event to the sorting data fetch unit 312.

When the completion event is transferred from the completion eventreception unit 311, the sorting data fetch unit 312 may search forsorting data corresponding to the completion event in the memory 220 andmay store the found sorting data in the memory queue 313. In anembodiment, by using key information included in the completion event,the sorting data fetch unit 312 may check which partition and directoryof the memory 2110 data corresponding to the completion event is storedin, and thus, may search for data stored in the memory 220 to store thefound data in the memory queue 313.

The memory queue 313 may temporarily store data, read by the sortingdata fetch unit 312, in a memory before storing the read data in the bigdata storage 320. The file creation unit 314 may create a physical fileincluding the data stored in the memory queue 313 and may store the filein the big data storage 320.

The error sensing data reception unit 315 may monitor the memory 220 ofthe distributed parallel processing system 200, and when a new errorsensing result is stored, the error sensing data reception unit 315 maystore the new error sensing result in the memory queue 313.

The big data storage 320 may store the file created by the file creationunit 314. The big data storage 320 may be implemented based on adistributed file system.

The big data storage 320 may be configured with a master node 320 a anda data node 320 b. The master node 320 a may store a lot of filescreated by the bid data analysis system 300 in a plurality of data nodes320 b, create and manage a job for searching for pieces of data storedin the data nodes 320 b, and manage metadata.

Here, the job may denote a unit for processing a query received from thequery processing unit 300 so as to search for the data stored in thedata node 320 b.

The metadata may include a location and a file name of the file storedin the data node 320 b, a block ID where the file is stored, and astorage location of a server. For example, when the file creation unit314 creates a file, a location and a file name of the file may be storedin the metadata, and in a case where a corresponding file is greaterthan a block size and thus is divided into five blocks stored in threedifferent servers, fifteen block IDs and a storage location of each ofthe servers may be additionally stored in the metadata.

In performing a job of searching for data stored in the data node 320 b,when distribution is performed on each job and data of a specific fileis loaded, the metadata may be used as location information about thedata.

A lot of files created by the big data analysis system 300 may be storedin the data node 320 b. The data node 320 b may be provided inplurality, and each of the plurality of data nodes 320 b may include ahistorical data storage 322 and a model storage 324.

The historical data storage 322 included in each of the data nodes 320 bmay store a large amount of collection data collected by the datacollection system 1 in real time, in addition to the file created by thefile creation unit 314. In an embodiment, the file created by the filecreation unit 314 may be separately stored in a relational database(RDB).

The model storage 324 may store a quality determination model and anerror prediction model which are necessary for determining the qualityof a material or a product.

The query processing unit 330 may be an element that searches for datastored in the big data storage 320 and returns the found data, and mayinclude at least one of a query reception unit 332, a query executionunit 336, and a query result transmission unit 338. The query processingunit 330 may further include a query scheduling unit 334.

The query reception unit 332 may receive a query from a user and mayinterpret a received query sentence.

The query execution unit 336 may transfer the query, received throughthe query reception unit 322, to the big data storage 320 to allow thequery to be executed, and thus, may obtain a query execution result fromthe big data storage 320.

The query result transmission unit 338 may transfer data, obtained asthe query execution result from the big data storage 320, to the userwho has requested a corresponding query.

If the query received through the query reception unit 332 consists of aplurality of lower queries, the query scheduling unit 334 may classifythe received query into each of the lower queries and may transfer theclassified query to the query execution unit 336.

FIG. 9 is a diagram illustrating in detail a configuration of a big dataanalysis system 300 according to another embodiment of the presentdisclosure.

As illustrated in FIG. 9, the big data analysis system 300 may includemay include a data processing unit 310, a big data storage 320, and aquery processing unit 330. The big data storage 320 and the queryprocessing unit 330 are the same as the elements illustrated in FIG. 8,and thus, their detailed descriptions are omitted. Hereinafter, onlyelements, differing from the elements illustrated in FIG. 8, of elementsof the data processing unit 310 will be described.

The data processing unit 310 according to another embodiment of thepresent disclosure may include at least one of a completion eventreception unit 311, a first sorting data fetch unit 312 a, a secondsorting data fetch unit 312 b, a data division unit 312 c, a memoryqueue 313, a plurality of file creation units 314 a to 314 h, and anerror sensing data reception unit 315.

Functions of the completion event reception unit 311, the memory queue313, and the error sensing data reception unit 315 are the same as thoseof the elements illustrated in FIG. 8, and thus, their detaileddescriptions are omitted.

The first sorting data fetch unit 312 a may read load data, mapped to anequipment ID and a material ID, from a memory 220. In the load data, asillustrated in FIG. 10, a data change occurs, and the load data has acharacteristic where a data change width is large. For example,temperature data or flatness data which is obtained while a RoughingMill (RM) or a Finishing Mill (FM) is processing an as-rolled plate in athick plate process may correspond to the load data.

The second sorting data fetch unit 312 b may read no-load data, which anequipment ID and a material ID are not mapped to, from the memory 220.Since the no-load data is measured in a state where a job is notperformed, as illustrated in FIG. 10, the no-load data has acharacteristic where the same value is continuously generated. Forexample, temperature data or flatness data which is obtained in a statewhere the Roughing Mill (RM) or the Finishing Mill (FM) does not processthe as-rolled plate in the thick plate process may correspond to theno-load data.

The first sorting data fetch unit 312 a and the second sorting datafetch unit 312 b may each be provided in plurality, for more enhancing aprocessing speed.

In this case, in the memory 220, the load data may be stored in a firststorage area (not shown), and the no-load data may be stored in a secondstorage area (not shown) independently of the load data.

Moreover, when an even where storing of the load data in the firststorage area is completed occurs, the completion event performing unit311 may transfer the event to the first sorting data fetch unit 312 a toallow the first sorting data fetch unit 312 a to read the load data fromthe first storage area. Also, when an even where storing of the no-loaddata in the second storage area is completed occurs, the completionevent performing unit 311 may transfer the event to the second sortingdata fetch unit 312 b to allow the second sorting data fetch unit 312 bto read the no-load data from the second storage area.

By using key information included in the completion event, the first orsecond sorting data fetch unit 312 a or 312 b may check which partitionand directory of the first or second storage area data corresponding tothe completion event is stored in, and thus, may read the load datastored in the first storage area or the no-load data stored in thesecond storage area.

In the above-described embodiment, the first and second storage areasmay each be implemented in a queue form. An event may be stored in aqueue space in the first and second storage areas, and the completionevent reception unit 311 may fetch the event from the queue space.Therefore, even though the completion event reception unit 311 isdowned, if the completion event reception unit 311 is recovered, anevent which is being previously processed may be preferentiallyprocessed, thereby preventing a loss of an event.

Moreover, in the above-described embodiment, each of the file creationunits 314 a to 314 n may create a file including the load data and mayrecord the file in a load data table (not shown) of a historical datastorage 322. Also, each of the file creation units 314 a to 314 n maycreate a file including the no-load data and may record the file in ano-load data table (not shown) of the historical data storage 322.

The data division unit 312 c may divide, by a predetermined data numberunit, the load data read by the first sorting data fetch unit 312 a orthe no-load data read by the second sorting data fetch unit 312 b andmay store the divided load data or no-load data in the memory queue 313.

The reason that the big data analysis system 300 according to anotherembodiment of the present disclosure divides data by the predetermineddata number unit by using the data division unit 312 c is because ifmassive data is simultaneously transferred to the memory queue 313, anout of memory occurs, and for this reason, a system is downed.

The file creation units 314 a to 314 n may create a physical fileincluding data stored in the memory queue 313. As illustrated in FIG. 9,since the big data analysis system 300 according to another embodimentof the present disclosure is implemented with the plurality to filecreation units 314 a to 314 n, the plurality if file creation units 314a to 314 n may perform a file creation job in parallel, therebyenhancing a speed of the file creation job. In such an embodiment, thefile creation units 314 a to 314 n may be clustered.

As described above, according to the embodiments of the presentdisclosure, data obtained in a continuous process may be processed inreal time, and moreover, massive data may be processed.

Moreover, according to the embodiments of the present disclosure, a loadof each processing unit may be controlled based on system resource useinformation based on execution of a performing unit distributed to eachprocessing unit, thereby enhancing system processing performance.

Moreover, according to the embodiments of the present disclosure, thememory may be triplicated with one master instance and two slaveinstances, thereby increasing an availability of the memory.

Moreover, according to the embodiments of the present disclosure, dataobtained in a continuous process may be stored in a big data storagebased on the distributed file system, and thus, the obtained data may beprocessed in real time.

Moreover, according to the embodiments of the present disclosure, dataobtained in a continuous process may be classified into load data andno-load data and processed, thereby improving a file search speed andshortening a query performing time.

Moreover, according to the embodiments of the present disclosure, dataobtained in a continuous process may be divided and stored by apredetermined data number unit, thereby preventing the occurrence of anout of memory of a memory queue.

Moreover, according to the embodiments of the present disclosure, thefile generation unit that processes data, obtained in a continuousprocess, into a file may be provided in plurality, thereby moreenhancing a processing speed.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the present disclosurewithout departing from the spirit or scope of the disclosures. Thus, itis intended that the present disclosure covers the modifications andvariations of this disclosure provided they come within the scope of theappended claims and their equivalents.

What is claimed is:
 1. A smart factory platform that processes data froma continuous process including a plurality of continuously-performedprocesses that produce a finished product using raw materials, the smartfactory platform comprising: a distributed parallel processing systemincluding at least one processing unit that generates mapping data bymapping a process identification (ID) to collection data collected fromthe continuous process and sorts the mapping data to generate sortingdata, the process ID being one of an equipment ID of equipmentperforming one of the plurality of continuously-performed processeswhere the collection data occurs and a material ID of a materialprocessed through the equipment, the at least one processing unitcomprising a material mapping performing unit mapping the material ID tothe collection data and an equipment mapping performing unit mapping theequipment ID to the collection data, and the sorting data beinggenerated to enable and perform association processing on pieces of thecollection data collected from different ones of thecontinuously-performed processes; and a big data analysis system storingthe sorting data with respect to the process ID and comprising a dataprocessing unit (i) dividing the sorting data into load data and no-loaddata and (ii) respectively storing the load data and the no-load data indifferent tables, wherein the load data is data collected when thematerial is processed or data that changes over a range, the no-loaddata is data collected when the material is not processed or where datahaving a same value is continuously generated, the material ID is mappedto the load data, and the material ID is not mapped to the no-load data,wherein a specific process of the plurality of continuously-performedprocesses changes physical properties of an output product, and asubsequent process of the plurality of continuously-performed processesuses the output product having the changed physical properties as one ofthe raw materials, and the at least one processing unit furthercomprises a data sorting performing unit that calculates a measurementvalue at each of a plurality of reference points in the material andcollection positions of the sorting data to generate reference data ateach of the reference points for the association processing of thecollection data collected from the specific process and the subsequentprocess at different periods.
 2. The smart factory platform of claim 1,wherein the at least one processing unit maps the material ID of thematerial processed through the equipment.
 3. The smart factory platformof claim 1, wherein the distributed parallel processing system furthercomprises a performing unit manager distributing the mapping of theprocess ID and the sorting of the mapping data to the at least oneprocessing unit to allow distributed mapping and sorting to beperformed, based on a load amount of the at least one processing unit.4. The smart factory platform of claim 3, wherein the performing unitmanager distributes the mapping of the process ID and the sorting of themapping data for the load amount of the at least one processing unit tobe controlled, based on at least one of (i) a use pattern of systemresources and (ii) an average value of the system resources based onexecution of one of more performing units distributed to the at leastone processing unit.
 5. The smart factory platform of claim 2, whereinthe equipment mapping performing unit maps the equipment ID, extractedbased on a collection time when the collection data is collected orattribute information about a sensor from which the collection data iscollected, to the collection data.
 6. The smart factory platform ofclaim 2, wherein the material mapping performing unit maps the materialID, extracted based on job instruction information for each process, tothe collection data.
 7. The smart factory platform of claim 1, whereinthe data sorting performing unit performs at least one of time sortingwhere pieces of mapping data are sorted based on a collection time andunit sorting where pieces of mapping data are sorted by a material unitprocessed in the process.
 8. The smart factory platform of claim 7,wherein the data sorting performing unit sequentially time-sorts mappingdata to which a same material ID is mapped among the pieces of mappingdata based on the collection time, or unit-sorts the mapping data withrespect to a corresponding one of the collection positions from whichthe collection data is collected.
 9. The smart factory platform of claim8, wherein the collection position is determined based on at least oneof a length of the material, a moving speed of the material, and acollection period of the collection data.
 10. The smart factory platformof claim 7, wherein the data sorting performing unit unit-sorts themapping data and pieces of the reference data in one direction in thematerial, and the pieces of the reference data are determined based onthe collection positions of the collection data and a reference point ofthe material.
 11. The smart factory platform of claim 10, wherein theone direction in the material is at least one of a lengthwise directionof the material, a widthwise direction of the material, and a thicknessdirection of the material.
 12. The smart factory platform of claim 10,wherein first reference data corresponding to the material processed inthe specific process and second reference data corresponding to thematerial processed in the subsequent process are associated with eachother, based on a first material ID included in the first reference dataand a second material ID included in the second reference data.
 13. Thesmart factory platform of claim 7, wherein the data sorting performingunit determines the collection positions from which the collection dataare collected based on a length of the material, a moving speed of thematerial, and a collection period of the collection data, and unit-sortsthe mapping data based on the reference data, calculated based on thereference points at predetermined intervals in the material and adistance between a plurality of the collection positions at each of thereference points.
 14. The smart factory platform of claim 1, wherein thedata processing unit divides the load data by a predetermined datanumber unit and stores the divided load data in a memory queue.
 15. Thesmart factory platform of claim 1, wherein the data processing unitcomprises: a file creation unit creating a file including the load data;a plurality of data nodes storing the file created by the file creationunit; and a master node distributing and storing the file created by thefile creation unit to and in the plurality of data nodes, and inresponse to a query for searching for the file in the plurality of datanodes, the master node generates and manages a job, which is a unit forprocessing the query.
 16. The smart factory platform of claim 1, whereinthe at least one processing unit generates the mapping data by mappingthe process ID to standardization data and sorts the mapping data to (i)enable association analysis on manufacturing-equipment-quality dataand/or (ii) predict omission data omitted during a collection period orat a point where data is not obtained.
 17. The smart factory platform ofclaim 1, wherein the continuous process is a steel process.
 18. Thesmart factory platform of claim 1, wherein the steel process includes aniron making process, a steel making process, a continuous castingprocess, and a rolling process.
 19. The smart factory platform of claim1, further comprising a data collection system, the data collectionsystem comprising (i) a plurality of measuring instruments or sensorsthat collect data generated in each of the continuously-performedprocesses and (ii) a programmable controller, a programmable logiccontroller (PLC), and/or a distributed control system (DCS) thatintegrates or controls the collection data obtained from the measuringinstruments or sensors.
 20. The smart factory platform of claim 1,further comprising a network system that transfers the collection datato the distributed parallel processing system, wherein the networksystem includes a network cable, a gateway, a router, and an accesspoint.
 21. The smart factory platform of claim 1, wherein thedistributed parallel processing system further includes a middlewaresystem that preprocesses the collection data, the middleware systemincluding an interface unit and a queue server.
 22. The smart factoryplatform of claim 21, wherein the interface unit standardizes thecollection data to generate standardization data, and the queue serverstores the standardization data.
 23. The smart factory platform of claim1, wherein the equipment mapping performing unit maps the equipment IDto standardization data to generate part of the mapping data, whereinthe standardization data is obtained from the equipment.